Computer Sciences Commons^™

Multiplencc: Inverse Probability Weighting Of Nested Case-Control Data, Nathalie C. Støer, Sven Ove Samuelsen

The R Journal

Reuse of controls from nested case-control designs can increase efficiency in many situations, for instance with competing risks or in other multiple endpoints situations. The matching between cases and controls must be broken when controls are to be used for other endpoints. A weighted analysis can then be performed to take care of the biased sampling from the cohort. We present the R package multipleNCC for reuse of controls in nested case-control studies by inverse probability weighting of the partial likelihood. The package handles right-censored, left-truncated and additionally matched data, and varying numbers of sampled controls and the whole analysis …

Editorial, Michael Lawrence

The R Journal

On behalf of the editorial board, I am pleased to publish Volume 8, Issue 2 of the R Journal. This issue contains 33 contributed research articles. Each of them either presents an R package, a specific extension of an R package or applications using R packages available from the Comprehensive R Archive Network (CRAN, http:://CRAN.R-project.org). This issue highlights the breadth and depth of the R package ecosystem, covering advances in statistical computing and visualization, as well as novel applications of R in specific domains. The authors have described a small but representative sample of the now more than 11000 packages …

Normal Tolerance Interval Procedures In The Tolerance Package, Derek S. Young

The R Journal

Statistical tolerance intervals are used for a broad range of applications, such as quality control, engineering design tests, environmental monitoring, and bioequivalence testing. tolerance is the only R package devoted to procedures for tolerance intervals and regions. Perhaps the most commonly-employed functions of the package involve normal tolerance intervals. A number of new procedures for this setting have been included in recent versions of tolerance. In this paper, we discuss and illustrate the functions that implement these normal tolerance interval procedures, one of which is a new, novel type of operating characteristic curve.

Micompr: An R Package For Multivariate Independent Comparison Of Observations, Nuno Fachada, João Rodrigues, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa

The R Journal

The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations

Rnrfa: An R Package To Retrieve, Filter And Visualize Data From The Uk National River Flow Archive, Claudia Vitolo, Matthew Fry, Wouter Buytaert

The R Journal

The UK National River Flow Archive (NRFA) stores several types of hydrological data and metadata: daily river flow and catchment rainfall time series, gauging station and catchment information. Data are served through the NRFA webservices via experimental RESTful APIs. Obtaining NRFA data can be unwieldy due to complexities in handling HTTP GET requests and parsing responses in JSON and XML formats. The rnrfa package provides a set of functions to programmatically access, filter, and visualize NRFA data using simple R syntax. This paper describes the structure of the rnrfa package, including examples using the main functions gdf() and cmr() for …

Simulating Correlated Binary And Multinomial Responses Under Marginal Model Specification: The Simcormultres Package, Anestis Touloumis

The R Journal

We developed the R package SimCorMultRes to facilitate simulation of correlated categorical (binary and multinomial) responses under a desired marginal model specification. The simulated correlated categorical responses are obtained by applying threshold approaches to correlated continuous responses of underlying regression models and the dependence structure is parametrized in terms of the correlation matrix of the latent continuous responses. This article provides an elaborate introduction to the SimCorMultRes package demonstrating its design and usage via three examples. The package can be obtained via CRAN.

Nmfgpu4r: Gpu-Accelerated Computation Of The Non-Negative Matrix Factorization (Nmf) Using Cuda Capable Hardware, Sven Koitka, Christoph M. Friedrich

The R Journal

In this work, a novel package called nmfgpu4R is presented, which offers the computation of Non-negative Matrix Factorization (NMF) on Compute Unified Device Architecture (CUDA) platforms within the R environment. Benchmarks show a remarkable speed-up in terms of time per iteration by utilizing the parallelization capabilities of modern graphics cards. Therefore the application of NMFgets more attractive for real-world sized problems because the time to compute a factorization is reduced by an order of magnitude.

Comf: An R Package For Thermal Comfort Studies, Marcel Schweiker

The R Journal

The field of thermal comfort generated a number of thermal comfort indices. Their code implementation needs to be done by individual researchers. This paper presents the R package, comf, which includes functions for common and new thermal comfort indices. Additional functions allow comparisons between the predictive performance of these indices. This paper reviews existing thermal comfort indices and available code implementations. This is followed by the description of the R package and an example how to use the R package for the comparison of different thermal comfort indices on data from a thermal comfort study.

An Introduction To Principal Surrogate Evaluation With The Pseval Package, Michael C. Sachs, Erin E. Gabriel

The R Journal

We describe a new package called pseval that implements the core methods for the evaluation of principal surrogates in a single clinical trial. It provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation methods are provided, including print, summary, plot, and testing. We summarize the main statistical methods that are implemented in the package and illustrate its use from the perspective …

Quantreg.Nonpar: An R Package For Performing Nonparametric Series Quantile Regression, Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov, Iván Fernández-Val

The R Journal

The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides point-wise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.

Two-Tier Latent Class Irt Models In R, Silvia Bacci, Francesco Bartolucci

The R Journal

In analyzing data deriving from the administration of a questionnaire to a group of individuals, Item Response Theory (IRT) models provide a flexible framework to account for several aspects involved in the response process, such as the existence of multiple latent traits. In this paper, we focus on a class of semi-parametric multidimensional IRT models, in which these traits are represented through one or more discrete latent variables; these models allow us to cluster individuals into homogeneous latent classes and, at the same time, to properly study item characteristics. In particular, we follow a within-item multidimensional formulation similar to that …

Qtools: A Collection Of Models And Tools For Quantile Inference, Marco Geraci

The R Journal

Quantiles play a fundamental role in statistics. The quantile function defines the distribution of a random variable and, thus, provides a way to describe the data that is specular but equivalent to that given by the corresponding cumulative distribution function. There are many advantages in working with quantiles, starting from their properties. The renewed interest in their usage seen in the last years is due to the theoretical, methodological, and software contributions that have broadened their applicability. This paper presents the R package Qtools, a collection of utilities for unconditional and conditional quantiles.

R Foundation News, Torsten Hothorn

The R Journal

New benefectors

Donations

New supporting institutions

New supporting members

Ggfortify: Unified Interface To Visualize Statistical Results Of Popular R Packages, Yuan Yuan, Masaaki Horikoshi, Wenxuan Li

The R Journal

The ggfortify package provides a unified interface that enables users to use one line of code to visualize statistical results of many R packages using ggplot2 idioms. With the help of ggfortify, statisticians, data scientists, and researchers can avoid the sometimes repetitive work of using the ggplot2 syntax to achieve what they need.

Ake: An R Package For Discrete And Continuous Associated Kernel Estimations, Wanbitching E. Wansouwé, Sobom M. Somé, Célestin C. Kokonendji

The R Journal

Kernel estimation is an important technique in exploratory data analysis. Its utility relies on its ease of interpretation, especially based on graphical means. The Ake package is introduced for univariate density or probability mass function estimation and also for continuous and discrete regression functions using associated kernel estimators. These associated kernels have been proposed due to their specific features of variables of interest. The package focuses on associated kernel methods appropriate for continuous (bounded, positive) or discrete (count, categorical) data often found in applied settings. Furthermore, optimal bandwidths are selected by cross-validation for any associated kernel and by Bayesian methods …

Eicompare: Comparing Ecological Inference Estimates Across Ei And Ei:Rc, Loren Collingwood, Kassra Oskooii, Sergio Garcia-Rios, Matt Barreto

The R Journal

Social scientists and statisticians often use aggregate data to predict individual-level behavior because the latter are not always available. Various statistical techniques have been developed to make inferences from one level (e.g., precinct) to another level (e.g., individual voter) that minimize errors associated with ecological inference. While ecological inference has been shown to be highly problematic in a wide array of scientific fields, many political scientists and analysis employ the techniques when studying voting patterns. Indeed, federal voting rights lawsuits now require such an analysis, yet expert reports are not consistent in which type of ecological inference is used. This …

Calculating Biological Module Enrichment Or Depletion And Visualizing Data On Large-Scale Molecular Maps With Acsnminer And Rnavicell Packages, Paul Deveau, Emmanuel Barillot, Valentina Boeva, Andrei Zinovyev, Eric Bonnet

The R Journal

Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, the Atlas of Cancer Signaling Network (ACSN) being an example. All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSNmolecular pathways gene sets, but can use any gene set encoded as a GMT file, …

Escape From Boxland, Barret Schloerke, Hadley Wickham, Dianne Cook, Heike Hofmann

The R Journal

A library of common geometric shapes can be used to train our brains for understanding data structure in high-dimensional Euclidean space. This article describes the methods for producing cubes, spheres, simplexes, and tori in multiple dimensions. It also describes new ways to define and generate high-dimensional tori. The algorithms are described, critical code chunks are given, and a large collection of generated data are provided. These are available in the R package geozoo, and selected movies and images, are available on the GeoZoo web site (http://schloerke.github.io/geozoo/)

Changes On Cran, Kurt Hornik, Achim Zeileis

The R Journal

CRAN growth

New CRAN task views

New packages in CRAN task views

Easyroc: An Interactive Web-Tool For Roc Curve Analysis Using R Language Environment, Dincer Goksuluk, Selcuk Korkmaz, Gokmen Zararsiz, A Ergun Karaagaoglu

The R Journal

ROC curve analysis is a fundamental tool for evaluating the performance of a marker in a number of research areas, e.g., biomedicine, bioinformatics, engineering etc., and is frequently used for discriminating cases from controls. There are a number of analysis tools which are used to guide researchers through their analysis. Some of these tools are commercial and provide basic methods for ROC curve analysis while others offer advanced analysis techniques and a command-based user interface, such as the R environment. The R environment includes comprehensive tools for ROC curve analysis; however, using a command-based interface might be challenging and time …

Hdm: High-Dimensional Metrics, Victor Chernozhukov, Chris Hansen, Martin Spindler

The R Journal

In this article the package High-dimensional Metrics hdm is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and average treatment effect for the treated (ATET), as well for extensions of these param eters to the endogenous setting are provided. Theory …

Distance Measures For Time Series In R: The Tsdist Package, Usue Mori, Alexander Mendiburu, Jose A. Lozano

The R Journal

The definition of a distance measure between time series is crucial for many time series data mining tasks, such as clustering and classification. For this reason, a vast portfolio of time series distance measures has been published in the past few years. In this paper, the TSdist package is presented, a complete tool which provides a unified framework to calculate the largest variety of time series dissimilarity measures available in R at the moment, to the best of our knowledge. The package implements some popular distance measures which were not previously available in R, and moreover, it also provides wrappers …

Condsurv: An R Package For The Estimation Of The Conditional Survival Function For Ordered Multivariate Failure Time Data, Luis Meira-Machado, Meira-Machado Sestelo

The R Journal

One major goal in clinical applications of time-to-event data is the estimation of survival with censored data. The usual nonparametric estimator of the survival function is the time-honored Kaplan-Meier product-limit estimator. Though this estimator has been implemented in several R packages, the development of the condSURV R package has been motivated by recent contributions that allow the estimation of the survival function for ordered multivariate failure time data. The condSURV package provides three different approaches all based on the Kaplan-Meier estimator. In one of these approaches these quantities are estimated conditionally on current or past covariate measures. Illustration of the …

Measurement Units In R, Edzer Pebesma, Thomas Mailund, James Hiebert

The R Journal

We briefly review SI units, and discuss R packages that deal with measurement units, their compatibility and conversion. Built upon udunits2 and the UNIDATA udunits library, we introduce the package units that provides a class for maintaining unit metadata. When used in expression, it automatically converts units, and simplifies units of results when possible; in case of incompatible units, errors are raised. The class flexibly allows expansion beyond predefined units. Using units may eliminate a whole class of potential scientific programming mistakes. We discuss the potential and limitations of computing with explicit units.

Tigris: An R Package To Access And Work With Geographic Data From The Us Census Bureau, Kyle Walker

The R Journal

TIGER/Line shapefiles from the United States Census Bureau are commonly used for the mapping and analysis of US demographic trends. The tigris package provides a uniform interface for R users to download and work with these shapefiles. Functions in tigris allow R users to request Census geographic datasets using familiar geographic identifiers and return those datasets as objects of class "Spatial*DataFrame". In turn, tigris ensures consistent and high-quality spatial data for R users’ cartographic and spatial analysis projects that involve US Census data. This article provides an overview of the functionality of the tigris package, and concludes with an applied …

Mixtox: An R Package For Mixture Toxicity Assessment, Xiang-Wei Zhu, Jian-Yi Chen

The R Journal

Mixture toxicity assessment is indeed necessary for humans and ecosystems that are continually exposed to a variety of chemical mixtures. This paper describes an R package, called mixtox, which offers a general framework of curve fitting, mixture experimental design, and mixture toxicity prediction for practitioners in toxicology. The unique features of mixtox include: (1) constructing a uniform table for mixture experimental design; and (2) predicting toxicity of a mixture with multiple components based on reference models such as concentration addition, independent action, and generalized concentration addition. We describe the various functions of the package and provide examples to illustrate their …

Water: Tools And Functions To Estimate Actual Evapotranspiration Using Land Surface Energy Balance Models In R, Guillermo Federico Olmedo, Samuel Ortega-Farías, Daniel De La Fuente-Sáiz, David Fonseca- Luego, Fernando Fuentes-Peñailillo

The R Journal

The crop water requirement is a key factor in the agricultural process. It is usually estimated throughout actual evapotranspiration (ETa). This parameter is the key to develop irrigation strategies, to improve water use efficiency and to understand hydrological, climatic, and ecosystem processes. Currently, it is calculated with classical methods, which are difficult to extrapolate, or with land surface energy balance models (LSEB), such as METRIC and SEBAL, which are based on remote sensing data. This paper describes water, an open implementation of LSEB. The package provides several functions to estimate the parameters of the LSEB equation from satellite data …

Semeo: A Semantic Equivalence Analysis Framework For Obfuscated Android Applications, Zhen Hu

School of Computing: Dissertations, Theses, and Student Research

Software repackaging is a common approach for creating malware. In this approach, malware authors inject malicious payloads into legitimate applications; then, to ren- der security analysis more difficult, they obfuscate most or all of the code. This forces analysts to spend a large amount of effort filtering out benign obfuscated methods in order to locate potentially malicious methods for further analysis. If an effective mechanism for filtering out benign obfuscated methods were available, the number of methods that must be analyzed could be reduced, allowing analysts to be more productive. In this thesis, we introduce SEMEO, a highly effective and …

On Path Consistency For Binary Constraint Satisfaction Problems, Christopher G. Reeson

School of Computing: Dissertations, Theses, and Student Research

Constraint satisfaction problems (CSPs) provide a flexible and powerful framework for modeling and solving many decision problems of practical importance. Consistency properties and the algorithms for enforcing them on a problem instance are at the heart of Constraint Processing and best distinguish this area from other areas concerned with the same combinatorial problems. In this thesis, we study path consistency (PC) and investigate several algorithms for enforcing it on binary finite CSPs. We also study algorithms for enforcing consistency properties that are related to PC but are stronger or weaker than PC.

We identify and correct errors in the literature …

Using Software Testing Techniques To Infer Biological Models, Mikaela Cashman

School of Computing: Dissertations, Theses, and Student Research

Years of research in software testing has given us novel ways to reason about and test the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. However, they use a unidirectional analogy – taking from nature without giving back.

In this thesis we invert this view and ask if we can utilize techniques from testing and modeling of highly-configurable software systems to aid in the emerging field of systems biology which aims to model and predict …